Overview

Dataset statistics

Number of variables13
Number of observations50000
Missing cells130218
Missing cells (%)20.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 MiB
Average record size in memory112.0 B

Variable types

DateTime1
Numeric9
Categorical1
Text2

Alerts

CO (mg/m3) is highly overall correlated with NO (ug/m3) and 4 other fieldsHigh correlation
Latitud is highly overall correlated with ProvinciaHigh correlation
Longitud is highly overall correlated with ProvinciaHigh correlation
NO (ug/m3) is highly overall correlated with CO (mg/m3) and 4 other fieldsHigh correlation
NO2 (ug/m3) is highly overall correlated with CO (mg/m3) and 4 other fieldsHigh correlation
PM10 (ug/m3) is highly overall correlated with CO (mg/m3) and 3 other fieldsHigh correlation
PM25 (ug/m3) is highly overall correlated with CO (mg/m3) and 3 other fieldsHigh correlation
Provincia is highly overall correlated with Latitud and 1 other fieldsHigh correlation
SO2 (ug/m3) is highly overall correlated with CO (mg/m3) and 2 other fieldsHigh correlation
CO (mg/m3) has 38700 (77.4%) missing valuesMissing
NO (ug/m3) has 3497 (7.0%) missing valuesMissing
NO2 (ug/m3) has 3709 (7.4%) missing valuesMissing
O3 (ug/m3) has 19006 (38.0%) missing valuesMissing
PM10 (ug/m3) has 11310 (22.6%) missing valuesMissing
PM25 (ug/m3) has 43991 (88.0%) missing valuesMissing
SO2 (ug/m3) has 9924 (19.8%) missing valuesMissing

Reproduction

Analysis started2025-12-03 03:12:49.992330
Analysis finished2025-12-03 03:13:00.707533
Duration10.72 seconds
Software versionydata-profiling vv4.18.0
Download configurationconfig.json

Variables

Fecha
Date

Distinct8708
Distinct (%)17.4%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
Minimum1997-01-01 00:00:00
Maximum2020-12-31 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-12-02T22:13:00.779390image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:13:00.910597image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

CO (mg/m3)
Real number (ℝ)

High correlation  Missing 

Distinct74
Distinct (%)0.7%
Missing38700
Missing (%)77.4%
Infinite0
Infinite (%)0.0%
Mean0.84640708
Minimum0
Maximum8.9
Zeros11
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-12-02T22:13:01.041220image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q10.3
median0.7
Q31.1
95-th percentile2.2
Maximum8.9
Range8.9
Interquartile range (IQR)0.8

Descriptive statistics

Standard deviation0.76506171
Coefficient of variation (CV)0.90389333
Kurtosis13.16064
Mean0.84640708
Median Absolute Deviation (MAD)0.4
Skewness2.6606167
Sum9564.4
Variance0.58531942
MonotonicityNot monotonic
2025-12-02T22:13:01.141633image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.11137
 
2.3%
0.2962
 
1.9%
0.4913
 
1.8%
0.3876
 
1.8%
0.5856
 
1.7%
0.6817
 
1.6%
0.7809
 
1.6%
0.8704
 
1.4%
0.9558
 
1.1%
1517
 
1.0%
Other values (64)3151
 
6.3%
(Missing)38700
77.4%
ValueCountFrequency (%)
011
 
< 0.1%
0.11137
2.3%
0.2962
1.9%
0.3876
1.8%
0.4913
1.8%
0.5856
1.7%
0.6817
1.6%
0.7809
1.6%
0.8704
1.4%
0.9558
1.1%
ValueCountFrequency (%)
8.91
< 0.1%
8.81
< 0.1%
8.61
< 0.1%
8.11
< 0.1%
7.91
< 0.1%
7.71
< 0.1%
7.41
< 0.1%
7.21
< 0.1%
7.11
< 0.1%
6.92
< 0.1%

NO (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct240
Distinct (%)0.5%
Missing3497
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean13.254629
Minimum0
Maximum378
Zeros415
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-12-02T22:13:01.241501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median5
Q315
95-th percentile51
Maximum378
Range378
Interquartile range (IQR)13

Descriptive statistics

Standard deviation21.892828
Coefficient of variation (CV)1.6517119
Kurtosis37.359431
Mean13.254629
Median Absolute Deviation (MAD)4
Skewness4.740571
Sum616380
Variance479.2959
MonotonicityNot monotonic
2025-12-02T22:13:01.339190image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17376
14.8%
26211
 
12.4%
34050
 
8.1%
43058
 
6.1%
52438
 
4.9%
61865
 
3.7%
71737
 
3.5%
81412
 
2.8%
91296
 
2.6%
101150
 
2.3%
Other values (230)15910
31.8%
(Missing)3497
 
7.0%
ValueCountFrequency (%)
0415
 
0.8%
17376
14.8%
26211
12.4%
34050
8.1%
43058
6.1%
52438
 
4.9%
61865
 
3.7%
71737
 
3.5%
81412
 
2.8%
91296
 
2.6%
ValueCountFrequency (%)
3781
< 0.1%
3671
< 0.1%
3531
< 0.1%
3501
< 0.1%
3462
< 0.1%
3401
< 0.1%
3191
< 0.1%
3161
< 0.1%
3121
< 0.1%
3111
< 0.1%

NO2 (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct160
Distinct (%)0.3%
Missing3709
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean21.44648
Minimum0
Maximum183
Zeros14
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-12-02T22:13:01.435909image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q18
median16
Q329
95-th percentile59
Maximum183
Range183
Interquartile range (IQR)21

Descriptive statistics

Standard deviation18.999379
Coefficient of variation (CV)0.88589733
Kurtosis5.3043838
Mean21.44648
Median Absolute Deviation (MAD)10
Skewness1.8676974
Sum992779
Variance360.97641
MonotonicityNot monotonic
2025-12-02T22:13:01.537922image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41702
 
3.4%
21646
 
3.3%
81621
 
3.2%
61621
 
3.2%
51613
 
3.2%
71604
 
3.2%
31547
 
3.1%
91516
 
3.0%
111474
 
2.9%
131431
 
2.9%
Other values (150)30516
61.0%
(Missing)3709
 
7.4%
ValueCountFrequency (%)
014
 
< 0.1%
11296
2.6%
21646
3.3%
31547
3.1%
41702
3.4%
51613
3.2%
61621
3.2%
71604
3.2%
81621
3.2%
91516
3.0%
ValueCountFrequency (%)
1831
 
< 0.1%
1781
 
< 0.1%
1691
 
< 0.1%
1681
 
< 0.1%
1653
< 0.1%
1641
 
< 0.1%
1632
< 0.1%
1561
 
< 0.1%
1542
< 0.1%
1533
< 0.1%

O3 (ug/m3)
Real number (ℝ)

Missing 

Distinct133
Distinct (%)0.4%
Missing19006
Missing (%)38.0%
Infinite0
Infinite (%)0.0%
Mean52.445344
Minimum0
Maximum725
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-12-02T22:13:01.641753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile14
Q137
median54
Q368
95-th percentile87
Maximum725
Range725
Interquartile range (IQR)31

Descriptive statistics

Standard deviation22.326237
Coefficient of variation (CV)0.42570485
Kurtosis28.411429
Mean52.445344
Median Absolute Deviation (MAD)15
Skewness0.97824251
Sum1625491
Variance498.46088
MonotonicityNot monotonic
2025-12-02T22:13:01.758754image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59595
 
1.2%
56591
 
1.2%
57586
 
1.2%
62586
 
1.2%
61570
 
1.1%
60557
 
1.1%
54542
 
1.1%
63539
 
1.1%
55538
 
1.1%
66538
 
1.1%
Other values (123)25352
50.7%
(Missing)19006
38.0%
ValueCountFrequency (%)
01
 
< 0.1%
132
 
0.1%
231
 
0.1%
361
0.1%
472
0.1%
5105
0.2%
6103
0.2%
7111
0.2%
8118
0.2%
9121
0.2%
ValueCountFrequency (%)
7251
 
< 0.1%
4061
 
< 0.1%
2911
 
< 0.1%
1401
 
< 0.1%
1331
 
< 0.1%
1301
 
< 0.1%
1283
< 0.1%
1252
< 0.1%
1244
< 0.1%
1232
< 0.1%

PM10 (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct174
Distinct (%)0.4%
Missing11310
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean22.719256
Minimum0
Maximum343
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-12-02T22:13:01.870290image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q111
median18
Q329
95-th percentile57
Maximum343
Range343
Interquartile range (IQR)18

Descriptive statistics

Standard deviation18.044606
Coefficient of variation (CV)0.79424284
Kurtosis14.580456
Mean22.719256
Median Absolute Deviation (MAD)8
Skewness2.5934769
Sum879008
Variance325.60781
MonotonicityNot monotonic
2025-12-02T22:13:01.973319image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
101650
 
3.3%
91605
 
3.2%
121530
 
3.1%
81520
 
3.0%
111485
 
3.0%
151419
 
2.8%
141412
 
2.8%
131397
 
2.8%
71354
 
2.7%
161336
 
2.7%
Other values (164)23982
48.0%
(Missing)11310
22.6%
ValueCountFrequency (%)
04
 
< 0.1%
1137
 
0.3%
2283
 
0.6%
3386
 
0.8%
4561
 
1.1%
5798
1.6%
61125
2.2%
71354
2.7%
81520
3.0%
91605
3.2%
ValueCountFrequency (%)
3431
< 0.1%
2901
< 0.1%
2381
< 0.1%
2301
< 0.1%
2262
< 0.1%
2251
< 0.1%
2241
< 0.1%
2211
< 0.1%
2041
< 0.1%
2031
< 0.1%

PM25 (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct119
Distinct (%)2.0%
Missing43991
Missing (%)88.0%
Infinite0
Infinite (%)0.0%
Mean13.842902
Minimum1
Maximum223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-12-02T22:13:02.070081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median9
Q315
95-th percentile45
Maximum223
Range222
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.074082
Coefficient of variation (CV)1.1611786
Kurtosis20.315948
Mean13.842902
Median Absolute Deviation (MAD)4
Skewness3.6670509
Sum83182
Variance258.37612
MonotonicityNot monotonic
2025-12-02T22:13:02.164081image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5422
 
0.8%
6420
 
0.8%
7387
 
0.8%
4383
 
0.8%
8373
 
0.7%
9364
 
0.7%
3344
 
0.7%
10318
 
0.6%
2315
 
0.6%
11306
 
0.6%
Other values (109)2377
 
4.8%
(Missing)43991
88.0%
ValueCountFrequency (%)
1124
 
0.2%
2315
0.6%
3344
0.7%
4383
0.8%
5422
0.8%
6420
0.8%
7387
0.8%
8373
0.7%
9364
0.7%
10318
0.6%
ValueCountFrequency (%)
2231
 
< 0.1%
1761
 
< 0.1%
1561
 
< 0.1%
1501
 
< 0.1%
1471
 
< 0.1%
1402
< 0.1%
1323
< 0.1%
1281
 
< 0.1%
1261
 
< 0.1%
1251
 
< 0.1%

SO2 (ug/m3)
Real number (ℝ)

High correlation  Missing 

Distinct158
Distinct (%)0.4%
Missing9924
Missing (%)19.8%
Infinite0
Infinite (%)0.0%
Mean9.0823435
Minimum-387
Maximum270
Zeros121
Zeros (%)0.2%
Negative3
Negative (%)< 0.1%
Memory size781.2 KiB
2025-12-02T22:13:02.256499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-387
5-th percentile1
Q12
median5
Q311
95-th percentile31
Maximum270
Range657
Interquartile range (IQR)9

Descriptive statistics

Standard deviation13.19505
Coefficient of variation (CV)1.4528244
Kurtosis90.825377
Mean9.0823435
Median Absolute Deviation (MAD)3
Skewness2.4198699
Sum363984
Variance174.10935
MonotonicityNot monotonic
2025-12-02T22:13:02.352542image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25273
10.5%
15021
10.0%
34320
8.6%
43744
 
7.5%
53030
 
6.1%
62368
 
4.7%
71993
 
4.0%
81679
 
3.4%
91368
 
2.7%
101126
 
2.3%
Other values (148)10154
20.3%
(Missing)9924
19.8%
ValueCountFrequency (%)
-3871
 
< 0.1%
-3861
 
< 0.1%
-3631
 
< 0.1%
0121
 
0.2%
15021
10.0%
25273
10.5%
34320
8.6%
43744
7.5%
53030
6.1%
62368
4.7%
ValueCountFrequency (%)
2701
< 0.1%
2291
< 0.1%
2161
< 0.1%
2111
< 0.1%
2061
< 0.1%
2021
< 0.1%
1992
< 0.1%
1881
< 0.1%
1871
< 0.1%
1851
< 0.1%

Provincia
Categorical

High correlation 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
León
19724 
Valladolid
8895 
Palencia
6956 
Burgos
6632 
Salamanca
3501 
Other values (5)
4292 

Length

Max length10
Median length9
Mean length6.38904
Min length4

Characters and Unicode

Total characters319452
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLeón
2nd rowLeón
3rd rowLeón
4th rowLeón
5th rowPalencia

Common Values

ValueCountFrequency (%)
León19724
39.4%
Valladolid8895
17.8%
Palencia6956
 
13.9%
Burgos6632
 
13.3%
Salamanca3501
 
7.0%
Soria1159
 
2.3%
Zamora995
 
2.0%
Segovia921
 
1.8%
Avila857
 
1.7%
Madrid360
 
0.7%

Length

2025-12-02T22:13:02.439533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-12-02T22:13:02.510834image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
león19724
39.4%
valladolid8895
17.8%
palencia6956
 
13.9%
burgos6632
 
13.3%
salamanca3501
 
7.0%
soria1159
 
2.3%
zamora995
 
2.0%
segovia921
 
1.8%
avila857
 
1.7%
madrid360
 
0.7%

Most occurring characters

ValueCountFrequency (%)
a50993
16.0%
l37999
11.9%
n30181
9.4%
e27601
8.6%
L19724
 
6.2%
ó19724
 
6.2%
i19148
 
6.0%
o18602
 
5.8%
d18510
 
5.8%
c10457
 
3.3%
Other values (13)66513
20.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)319452
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a50993
16.0%
l37999
11.9%
n30181
9.4%
e27601
8.6%
L19724
 
6.2%
ó19724
 
6.2%
i19148
 
6.0%
o18602
 
5.8%
d18510
 
5.8%
c10457
 
3.3%
Other values (13)66513
20.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)319452
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a50993
16.0%
l37999
11.9%
n30181
9.4%
e27601
8.6%
L19724
 
6.2%
ó19724
 
6.2%
i19148
 
6.0%
o18602
 
5.8%
d18510
 
5.8%
c10457
 
3.3%
Other values (13)66513
20.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)319452
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a50993
16.0%
l37999
11.9%
n30181
9.4%
e27601
8.6%
L19724
 
6.2%
ó19724
 
6.2%
i19148
 
6.0%
o18602
 
5.8%
d18510
 
5.8%
c10457
 
3.3%
Other values (13)66513
20.8%
Distinct91
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size781.2 KiB
2025-12-02T22:13:02.633761image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length32
Median length25
Mean length14.20862
Min length3

Characters and Unicode

Total characters710431
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowC.T.L.R. - Ventosilla
2nd rowC.T.Compostilla-San Miguel
3rd rowC.T.Compostilla-Villaverde
4th rowC.T.L.R. - Ventosilla
5th rowC.T.G. - Compuerto
ValueCountFrequency (%)
9211
 
9.0%
de6913
 
6.8%
c.t.anllares3903
 
3.8%
del2913
 
2.9%
c.t.l.r2463
 
2.4%
la2257
 
2.2%
miranda2203
 
2.2%
22103
 
2.1%
ii1910
 
1.9%
c.t.g1589
 
1.6%
Other values (112)66341
65.2%
2025-12-02T22:13:02.829007image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a78651
 
11.1%
l56677
 
8.0%
51806
 
7.3%
o51216
 
7.2%
e47457
 
6.7%
r37023
 
5.2%
n33512
 
4.7%
i33189
 
4.7%
.32805
 
4.6%
C25145
 
3.5%
Other values (43)262950
37.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)710431
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a78651
 
11.1%
l56677
 
8.0%
51806
 
7.3%
o51216
 
7.2%
e47457
 
6.7%
r37023
 
5.2%
n33512
 
4.7%
i33189
 
4.7%
.32805
 
4.6%
C25145
 
3.5%
Other values (43)262950
37.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)710431
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a78651
 
11.1%
l56677
 
8.0%
51806
 
7.3%
o51216
 
7.2%
e47457
 
6.7%
r37023
 
5.2%
n33512
 
4.7%
i33189
 
4.7%
.32805
 
4.6%
C25145
 
3.5%
Other values (43)262950
37.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)710431
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a78651
 
11.1%
l56677
 
8.0%
51806
 
7.3%
o51216
 
7.2%
e47457
 
6.7%
r37023
 
5.2%
n33512
 
4.7%
i33189
 
4.7%
.32805
 
4.6%
C25145
 
3.5%
Other values (43)262950
37.0%

Latitud
Real number (ℝ)

High correlation 

Distinct101
Distinct (%)0.2%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean42.150603
Minimum38.938333
Maximum43.603333
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size781.2 KiB
2025-12-02T22:13:02.919611image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum38.938333
5-th percentile40.949722
Q141.645556
median42.542778
Q342.688056
95-th percentile42.849167
Maximum43.603333
Range4.665
Interquartile range (IQR)1.0425

Descriptive statistics

Standard deviation0.66469456
Coefficient of variation (CV)0.015769515
Kurtosis-0.60406753
Mean42.150603
Median Absolute Deviation (MAD)0.30638889
Skewness-0.68328776
Sum2106392.1
Variance0.44181886
MonotonicityNot monotonic
2025-12-02T22:13:03.033292image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.949722221143
 
2.3%
41.31638889966
 
1.9%
41.67111111952
 
1.9%
42.33611111933
 
1.9%
41.61277778924
 
1.8%
41.96138889910
 
1.8%
41.60416667897
 
1.8%
42.79527778883
 
1.8%
42.68805556880
 
1.8%
42.68444444877
 
1.8%
Other values (91)40608
81.2%
ValueCountFrequency (%)
38.938333333
 
< 0.1%
40.38694444360
 
0.7%
40.5694444468
 
0.1%
40.57055556311
 
0.6%
40.65861111488
1.0%
40.66472222369
 
0.7%
40.949722221143
2.3%
40.95555556373
 
0.7%
40.9558333386
 
0.2%
40.9605555682
 
0.2%
ValueCountFrequency (%)
43.6033333380
 
0.2%
43.0416666781
 
0.2%
43.04111111326
 
0.7%
42.9525356
0.7%
42.9516666780
 
0.2%
42.94416667700
1.4%
42.87777778621
1.2%
42.84916667833
1.7%
42.84638889714
1.4%
42.8425252
 
0.5%

Longitud
Real number (ℝ)

High correlation 

Distinct100
Distinct (%)0.2%
Missing27
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean-5.1746525
Minimum-6.7819444
Maximum-2.4666667
Zeros0
Zeros (%)0.0%
Negative49973
Negative (%)99.9%
Memory size781.2 KiB
2025-12-02T22:13:03.137427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.7819444
5-th percentile-6.6622222
Q1-6.4838889
median-4.9091667
Q3-4.5383333
95-th percentile-2.9405556
Maximum-2.4666667
Range4.3152778
Interquartile range (IQR)1.9455556

Descriptive statistics

Standard deviation1.1246292
Coefficient of variation (CV)-0.21733425
Kurtosis-0.6422546
Mean-5.1746525
Median Absolute Deviation (MAD)0.75277778
Skewness0.34500614
Sum-258592.91
Variance1.2647909
MonotonicityNot monotonic
2025-12-02T22:13:03.220758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-6.7255555561391
 
2.8%
-6.5208333331299
 
2.6%
-4.909166667966
 
1.9%
-3.683888889952
 
1.9%
-3.636111111933
 
1.9%
-4.740833333924
 
1.8%
-4.494444444910
 
1.8%
-4.728888889897
 
1.8%
-4.840833333883
 
1.8%
-2.940555556880
 
1.8%
Other values (90)39938
79.9%
ValueCountFrequency (%)
-6.781944444720
1.4%
-6.7255555561391
2.8%
-6.662222222493
 
1.0%
-6.653611111452
 
0.9%
-6.643333333742
1.5%
-6.625192
 
0.4%
-6.603888889660
1.3%
-6.600277778520
 
1.0%
-6.589444444745
1.5%
-6.588888889316
 
0.6%
ValueCountFrequency (%)
-2.466666667627
1.3%
-2.480555556106
 
0.2%
-2.856944444426
0.9%
-2.9175877
1.8%
-2.91805555656
 
0.1%
-2.94027777891
 
0.2%
-2.940555556880
1.8%
-2.954444444299
 
0.6%
-3.475277778436
0.9%
-3.636111111933
1.9%
Distinct106
Distinct (%)0.2%
Missing27
Missing (%)0.1%
Memory size781.2 KiB
2025-12-02T22:13:03.546136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length28
Median length28
Mean length26.720689
Min length12

Characters and Unicode

Total characters1335313
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row42.9441666667,-5.66194444444
2nd row42.5969444444,-6.52083333333
3rd row42.6138888889,-6.48388888889
4th row42.9441666667,-5.66194444444
5th row42.8491666667,-4.83583333333
ValueCountFrequency (%)
41.3163888889,-4.90916666667966
 
1.9%
41.6711111111,-3.68388888889952
 
1.9%
42.3361111111,-3.63611111111933
 
1.9%
41.6127777778,-4.74083333333924
 
1.8%
41.9613888889,-4.49444444444910
 
1.8%
41.6041666667,-4.72888888889897
 
1.8%
42.7952777778,-4.84083333333883
 
1.8%
42.6880555556,-2.94055555556880
 
1.8%
42.6844444444,-2.9175877
 
1.8%
41.6,-4.7325858
 
1.7%
Other values (96)40893
81.8%
2025-12-02T22:13:03.769387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4170953
12.8%
8149495
11.2%
6138784
10.4%
5132995
10.0%
3127850
9.6%
1124526
9.3%
7110674
8.3%
2106875
8.0%
.99946
7.5%
,49973
 
3.7%
Other values (3)123242
9.2%

Most occurring categories

ValueCountFrequency (%)
(unknown)1335313
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
4170953
12.8%
8149495
11.2%
6138784
10.4%
5132995
10.0%
3127850
9.6%
1124526
9.3%
7110674
8.3%
2106875
8.0%
.99946
7.5%
,49973
 
3.7%
Other values (3)123242
9.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown)1335313
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
4170953
12.8%
8149495
11.2%
6138784
10.4%
5132995
10.0%
3127850
9.6%
1124526
9.3%
7110674
8.3%
2106875
8.0%
.99946
7.5%
,49973
 
3.7%
Other values (3)123242
9.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown)1335313
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
4170953
12.8%
8149495
11.2%
6138784
10.4%
5132995
10.0%
3127850
9.6%
1124526
9.3%
7110674
8.3%
2106875
8.0%
.99946
7.5%
,49973
 
3.7%
Other values (3)123242
9.2%

Interactions

2025-12-02T22:12:59.084888image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:51.949859image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.722377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.535259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.364119image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:55.498827image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.353978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.137550image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.118136image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:59.173521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.024315image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.827298image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.627392image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.465218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:55.592090image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.445724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.231739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.212649image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:59.268569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.109521image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.916564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.726658image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.558191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:55.685857image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.535108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.344064image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.328208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:59.362188image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.203326image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.009051image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.836419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.645552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:55.776086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.617826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.457286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.455814image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:59.454448image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.284724image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.096385image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.935205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.745422image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:55.874899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.698835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.599381image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.569892image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:59.566011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.366092image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.181784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.026567image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.847592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:55.964067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.775191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.710910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.663398image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:59.665906image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.447991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.261031image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.107718image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.936408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.056699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.856503image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.802442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.770067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:59.756766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.524468image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.351173image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.202520image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:55.039416image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.156055image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.937282image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.920607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.903970image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:59.839131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:52.605287image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:53.438941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:54.285697image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:55.174740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:56.258554image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:57.037405image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.018889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-12-02T22:12:58.996233image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-12-02T22:13:03.841555image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
CO (mg/m3)LatitudLongitudNO (ug/m3)NO2 (ug/m3)O3 (ug/m3)PM10 (ug/m3)PM25 (ug/m3)ProvinciaSO2 (ug/m3)
CO (mg/m3)1.000-0.033-0.0620.6040.595-0.2550.5870.6870.1300.645
Latitud-0.0331.000-0.294-0.218-0.224-0.042-0.232-0.4360.6660.067
Longitud-0.062-0.2941.0000.1290.1080.0530.218-0.0850.731-0.080
NO (ug/m3)0.604-0.2180.1291.0000.780-0.4780.5800.6230.0590.552
NO2 (ug/m3)0.595-0.2240.1080.7801.000-0.4460.5760.6610.0980.506
O3 (ug/m3)-0.255-0.0420.053-0.478-0.4461.000-0.155-0.2450.061-0.253
PM10 (ug/m3)0.587-0.2320.2180.5800.576-0.1551.0000.8550.0750.416
PM25 (ug/m3)0.687-0.436-0.0850.6230.661-0.2450.8551.0000.3280.499
Provincia0.1300.6660.7310.0590.0980.0610.0750.3281.0000.065
SO2 (ug/m3)0.6450.067-0.0800.5520.506-0.2530.4160.4990.0651.000

Missing values

2025-12-02T22:12:59.980371image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-12-02T22:13:00.121368image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-12-02T22:13:00.589335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

FechaCO (mg/m3)NO (ug/m3)NO2 (ug/m3)O3 (ug/m3)PM10 (ug/m3)PM25 (ug/m3)SO2 (ug/m3)ProvinciaEstaciónLatitudLongitudPosición
3411102004-04-11NaN10.021.0NaN6.0NaN8.0LeónC.T.L.R. - Ventosilla42.944167-5.66194442.9441666667,-5.66194444444
2341222009-02-27NaN6.020.0NaN21.0NaN6.0LeónC.T.Compostilla-San Miguel42.596944-6.52083342.5969444444,-6.52083333333
1456282013-06-09NaN5.018.0NaN10.0NaN18.0LeónC.T.Compostilla-Villaverde42.613889-6.48388942.6138888889,-6.48388888889
1976252010-12-11NaN2.016.026.09.0NaN8.0LeónC.T.L.R. - Ventosilla42.944167-5.66194442.9441666667,-5.66194444444
1028602015-07-22NaN6.09.056.09.07.013.0PalenciaC.T.G. - Compuerto42.849167-4.83583342.8491666667,-4.83583333333
2221552009-09-260.14.015.0NaN32.0NaN5.0BurgosBurgos142.350833-3.67555642.3508333333,-3.67555555556
1746142012-01-30NaN7.019.039.014.0NaNNaNBurgosBurgos542.345556-3.72111142.3455555556,-3.72111111111
1698122012-04-22NaNNaNNaNNaN11.0NaN1.0LeónOtero42.564444-6.78194442.5644444444,-6.78194444444
3549422003-08-31NaNNaNNaNNaN7.0NaNNaNValladolidVega Sicilia41.620556-4.74666741.6205555556,-4.74666666667
774382016-11-05NaNNaNNaN70.07.0NaNNaNValladolidVega Sicilia41.620556-4.74666741.6205555556,-4.74666666667
FechaCO (mg/m3)NO (ug/m3)NO2 (ug/m3)O3 (ug/m3)PM10 (ug/m3)PM25 (ug/m3)SO2 (ug/m3)ProvinciaEstaciónLatitudLongitudPosición
1721772012-03-12NaN8.019.053.022.0NaN2.0AvilaAvila II40.664722-4.70055640.6647222222,-4.70055555556
3810932002-04-12NaN33.050.026.054.0NaN29.0SalamancaSalamanca240.965278-5.65583340.9652777778,-5.65583333333
658532017-06-06NaN1.02.068.09.0NaN3.0LeónLa Robla42.802778-5.62361142.8027777778,-5.62361111111
3595522003-06-170.635.036.026.0NaNNaN7.0PalenciaPalencia242.003611-4.52472242.0036111111,-4.52472222222
3266632004-12-01NaN7.022.013.0NaNNaNNaNValladolidMichelin141.666389-4.71500041.6663888889,-4.715
4280401998-08-231.823.046.041.081.0NaN44.0LeónLeon142.603889-5.58722242.6038888889,-5.58722222222
3551162003-08-29NaN3.02.0NaNNaNNaN6.0LeónC.T.L.R. - Naredo42.816667-5.53333342.8166666667,-5.53333333333
4312321998-05-16NaN10.033.0112.0NaNNaN14.0BurgosMiranda de Ebro242.688056-2.94055642.6880555556,-2.94055555556
281582019-05-13NaN2.02.089.04.02.01.0PalenciaC.T.G. - Compuerto42.849167-4.83583342.8491666667,-4.83583333333
828522016-07-27NaN4.012.0NaN11.010.0NaNValladolidRenault341.612778-4.74083341.6127777778,-4.74083333333